Deep active object recognition by joint label and action prediction
By Mohsen Malmir, Karan Sikka, Deborah Forster, Ian R. Fasel, Javier R. Movellan, Garrison W. Cottrell. 2015
21 citations recorded by the source corpus.
An active object recognition system has the advantage of being able to act in the environment to capture images that are more suited for training and that lead to better performance at test time. In this paper, we propose a deep convolutional neural network for active object recognition that simultaneously predicts the object label, and selects the next action to perform on the object with the aim of improving recognition performance. We treat active object recognition as a reinforcement learning problem and derive the cost function to train the network for joint prediction of the object label and the action. A generative model of object similarities based on the Dirichlet distribution is proposed and embedded in the network for encoding the state of the system. The training is carried out by simultaneously minimizing the label and action prediction errors using gradient descent. We empirically show that the proposed network is able to predict both the object label and the actions on GERMS, a dataset for active object recognition. We compare the test label prediction accuracy of the proposed model with Dirichlet and Naive Bayes state encoding. The results of experiments suggest that the proposed model equipped with Dirichlet state encoding is superior in performance, and selects images that lead to better training and higher accuracy of label prediction at test time.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Sergey Levine
- Dieter Fox
- Pieter Abbeel
- Chelsea Finn
- Wolfram Burgard
- Trevor Darrell
- Lei Tai
- Michael Milford
In Computer Vision and Pattern Recognition
- Luc Van Gool
- Trevor Darrell
- Sergey Levine
- Jitendra Malik
- Andrew Zisserman
- Kristen Grauman
- Dacheng Tao
- Vladlen Koltun
Citations in the corpus
- The limits and potentials of deep learning for robotics (2018, background): “Some work is undertaken in the area of next-best view-point prediction to improve object detection [5], [25], [73], [130] or path planning for exploration on a mobile robot [14], but a more holistic approach to active scene understanding is still missing from current research.”